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| Fault diagnosis of wind power gearbox based on wavelet transform and improved CNN |
Zhu-peng WEN1( ),Jie CHEN1,2,*(),Lian-hua LIU1,Ling-ling JIAO1 |
1. School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China
2. Jiangsu Key Laboratory of Digital Manufacturing for Industrial Equipment and Control Technology, Nanjing 211816, China |
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Abstract An intelligent fault diagnosis method for wind turbine gearbox based on wavelet transform and two-dimensional densely connected dilated convolutional neural network(WT-ICNN) was proposed, aiming at the problem that traditional fault diagnosis method dependent on human experience too much. One dimensional vibration signal was transformed into two-dimensional fault image by continuous wavelet transform. Then the two-dimensional fault image was inputted into ICNN for training and testing. The verification of open source data of gearbox and measured data of wind field showed that compared with the traditional fault diagnosis methods, the proposed method effectively enhanced the utilization efficiency of fault features by using the densely connected structure for adaptive feature extraction of time-frequency map. And in the fault diagnosis of wind power gearbox, the proposed method had better feature reuse ability and higher diagnosis accuracy.
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Received: 25 June 2021
Published: 30 June 2022
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| Fund: 国家重点研发计划资助项目(2019YFB2005005) |
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Corresponding Authors:
Jie CHEN
E-mail: 1393532653@qq.com;njtechchenjie@163.com
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基于小波变换和优化CNN的风电齿轮箱故障诊断
针对传统故障诊断方法过于依赖人为经验的缺陷，提出小波变换和二维密集连接扩张卷积神经网络(WT-ICNN)的风电齿轮箱智能故障诊断方法. 所提方法将一维振动信号通过连续小波变换(WT)转换成二维故障图像；再将二维故障图像输入ICNN中进行训练和测试. 通过齿轮箱开源数据和风场实测数据验证结果表明，与传统故障诊断方法相比，所提方法采用密集连接的结构自适应特征提取时频图，有效加强了故障特征的利用效率；在对风电齿轮箱的故障诊断中，所提方法具有更好的特征复用能力和更高的诊断精度.
关键词：
风电齿轮箱,
小波变换,
卷积神经网络,
密集连接,
扩张卷积
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|
| [1] |
DAI J C, YANG X, WEN L Development of wind power industry in China: a comprehensive assessment[J]. Renewable and Sustainable Energy Reviews, 2018, 97: 156- 164
doi: 10.1016/j.rser.2018.08.044
|
|
|
| [2] |
LEI Y G, YANG B, JIANG X W, et al Applications of machine learning to machine fault diagnosis: a review and roadmap[J]. Mechanical Systems and Signal Processing, 2020, 138: 106587
doi: 10.1016/j.ymssp.2019.106587
|
|
|
| [3] |
张西宁, 郭清林, 刘书语. 深度学习技术及其故障诊断应用分析与展望[J]. 西安交通大学学报, 2020, 54(12): 1-13.
ZHANG Xi-ning, GUO Qing-lin, LIU Shu-yu. Analysis and prospect of deep learning technology and its fault diagnosis application [J]. Journal of Xi'an Jiaotong University. 2020, 54 (12): 1-13.
|
|
|
| [4] |
李恒, 张氢, 秦仙蓉, 等 基于短时傅里叶变换和卷积神经网络的轴承故障诊断方法[J]. 振动与冲击, 2018, 37 (19): 124- 131
LI Heng, ZHANG Qing, QIN Xian-rong, et al Fault diagnosis method for rolling bearings based on short-time Fourier transform and convolution neural network[J]. Journal of Vibration and Shock, 2018, 37 (19): 124- 131
|
|
|
| [5] |
陈仁祥, 黄鑫, 杨黎霞, 等 基于卷积神经网络和离散小波变换的滚动轴承故障诊断[J]. 振动工程学报, 2018, 31 (5): 883- 891
CHEN Ren-xiang, HUANG Xin, YANG Li-xia, et al Rolling bearing fault diagnosis based on convolution neural network and discrete wavelet transform[J]. Journal of Vibration Engineering, 2018, 31 (5): 883- 891
|
|
|
| [6] |
ZHAO D Z, WANG T Y, CHU F L Deep convolutional neural network based planet bearing fault classification[J]. Computers in Industry, 2019, 107: 59- 66
doi: 10.1016/j.compind.2019.02.001
|
|
|
| [7] |
LIANG P F, DENG C, WU J, et al Intelligent fault diagnosis of rotating machinery via wavelet transform, generative adversarial net and convolutional neural network[J]. Measurement, 2020, 159: 107768
doi: 10.1016/j.measurement.2020.107768
|
|
|
| [8] |
JIAO J Y, ZHAO M, LIN J, et al A multivariate encoder information based convolutional neural network for intelligent fault diagnosis of planetary gearboxes[J]. Knowledge-Based Systems, 2018, 160: 237- 250
doi: 10.1016/j.knosys.2018.07.017
|
|
|
| [9] |
JING L Y, ZHAO M, LI P, et al A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox[J]. Measurement, 2017, 111: 1- 10
doi: 10.1016/j.measurement.2017.07.017
|
|
|
| [10] |
WANG S X, CHEN H W A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network[J]. Applied Energy, 2019, 235: 1126- 1140
doi: 10.1016/j.apenergy.2018.09.160
|
|
|
| [11] |
于重重, 宁亚倩, 秦勇, 等 基于T-SNE样本熵和TCN的滚动轴承状态退化趋势预测[J]. 仪器仪表学报, 2019, 40 (8): 39- 46
YU Chong-chong, NING Ya-qian, QIN Yong, et al Prediction of rolling bearing state degradation trend based on T-SNE sample entropy and TCN[J]. Chinese Journal of Scientific Instrument, 2019, 40 (8): 39- 46
|
|
|
| [12] |
HAN Y, TANG B P, DENG L An enhanced convolutional neural network with enlarged receptive fields for fault diagnosis of planetary gearboxes[J]. Computers in Industry, 2019, 107: 50- 58
doi: 10.1016/j.compind.2019.01.012
|
|
|
| [13] |
SHAO S, MCALEER S, YAN R Q, et al Highly accurate machine fault diagnosis using deep transfer learning[J]. IEEE Transactions on Industrial Informatics, 2019, 15 (4): 2446- 2455
doi: 10.1109/TII.2018.2864759
|
|
|
| [14] |
邵思羽. 基于深度学习的旋转机械故障诊断方法研究[D]. 南京: 东南大学, 2019.
SHAO Si-yu. Methodologies for fault diagnosis of rotary machine based on deep learning [D]. Nanjing: Southeast University, 2019.
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